The U.S. Air Force trains and educates a large and diverse workforce to meet requirements across a complex array of missions. Early-stage training in many Air Force Specialty Codes can consist of large corpora of foundational, static material, so training that motivates airmen is a necessary element in helping ensure a supply of qualified personnel in mission-critical areas like aerospace maintenance. Games and game-play can improve engagement in computer-mediated learning, but needed is the ability to measure motivation and engagement. Metrics are needed to (1) identify which techniques and approaches offer the greatest efficacy; (2) enable learning contexts that can adapt to detected lapses in engagement. Building on previous work performed by Eduworks Corporation for AFRL, we propose to create the Observational Motivation and Engagement Generalized Appliance (OMEGA). OMEGA incorporates previously prototyped technology, combining adaptive tutoring and service oriented architectures with persistent learning, student modeling and adaptive recommendation. The result will provide both valid constructs and measures, and software to apply these metrics and generate recommendations. OMEGA will yield replicable and reliable metrics, providing persistent and unobtrusive assessments to enhance the Air Force training and education enterprise with adaptive support for learner engagement.Adaptive training,assessment,tutoring,learning,measurement.
